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B-TECH in Data Science at Shoolini University of Biotechnology and Management Sciences
Solan, Himachal Pradesh
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About the Specialization
What is Data Science at Shoolini University of Biotechnology and Management Sciences Solan?
This B.Tech Data Science program at Shoolini University of Biotechnology and Management Sciences, Solan, focuses on equipping students with expertise in statistical modeling, machine learning, and big data technologies. It is designed to meet the rapidly growing demand for skilled data professionals in the Indian industry, offering a comprehensive curriculum that blends theoretical foundations with practical application in emerging tech landscapes.
Who Should Apply?
This program is ideal for 10+2 graduates with a strong aptitude for mathematics and problem-solving, aspiring to build careers in data analytics, AI, and machine learning. It also suits working professionals with a technical background looking to upskill in data science, and career changers from related STEM fields eager to transition into the data-driven economy.
Why Choose This Course?
Graduates of this program can expect to secure roles such as Data Scientist, Machine Learning Engineer, Data Analyst, or AI Specialist in leading Indian and global companies. Entry-level salaries typically range from INR 4-7 LPA, with experienced professionals earning significantly more. The curriculum aligns with industry certifications and fosters a strong foundation for advanced studies and entrepreneurial ventures in India.
Student Success Practices
Foundation Stage
Master Core Programming and Mathematics- (Semester 1-2)
Actively engage in programming assignments using foundational languages like C/C++ and Python. Simultaneously, reinforce essential mathematical concepts such as Calculus, Linear Algebra, and Discrete Mathematics, which are critical for understanding data science algorithms.
Tools & Resources
HackerRank, LeetCode, Khan Academy, NPTEL for foundational math
Career Connection
Strong programming and mathematical skills are fundamental requirements for virtually all data science roles, providing the essential groundwork for comprehending and implementing complex algorithms in the industry.
Develop Structured Problem-Solving Skills- (Semester 1-2)
Practice systematically breaking down complex problems into smaller, more manageable components. Focus on cultivating logical thinking and designing efficient algorithms before coding, which is a crucial skill in data science workflows.
Tools & Resources
Flowcharting tools, Pseudocode practice platforms, Competitive programming websites
Career Connection
Enhances logical reasoning and analytical abilities, which are indispensable for formulating and designing effective data solutions, debugging intricate code, and optimizing processes in professional settings.
Build a Strong Peer Learning Network- (Semester 1-2)
Form active study groups with classmates to regularly discuss challenging concepts, collaborate on problem-solving exercises, and prepare effectively for examinations. This fosters a supportive and interactive learning environment.
Tools & Resources
University student forums, WhatsApp groups for academic discussion, Peer tutoring sessions facilitated by the institution
Career Connection
Cultivates essential teamwork and communication skills, which are vital for collaborative projects in professional environments. Additionally, it establishes a valuable academic and social support system for sustained learning.
Intermediate Stage
Engage in Practical Data Science Projects- (Semester 3-5)
Apply theoretical knowledge gained from Data Structures, Database Management Systems, and introductory Data Science courses to develop and implement small to medium-scale data science projects. This bridges the gap between theory and application.
Tools & Resources
Kaggle datasets, GitHub for version control and collaboration, Python libraries like Pandas and NumPy, Local or university-sponsored hackathons
Career Connection
These projects serve as tangible evidence of practical skills for potential employers and significantly contribute to building a robust portfolio, which is crucial for securing internships and subsequent placements.
Explore and Master Machine Learning Libraries- (Semester 4-5)
Deepen understanding and proficiency in key machine learning libraries such as scikit-learn, TensorFlow, and PyTorch. Focus on comprehending their underlying functionalities, application scenarios, and limitations through extensive hands-on practice and experimentation.
Tools & Resources
Official library documentation, Online courses from Coursera or Udacity specializing in ML frameworks, Google Colab for cloud-based experimentation
Career Connection
Proficiency in these industry-standard libraries is critical for effectively implementing and deploying machine learning models, a core competency required for Data Scientist and Machine Learning Engineer roles.
Seek Early Industry Exposure- (Semester 4-5)
Proactively pursue internships, attend industry-specific workshops, and participate in guest lectures delivered by industry experts. This helps in gaining an early understanding of real-world data science challenges and typical company workflows.
Tools & Resources
University placement cell services, LinkedIn for professional networking, Industry meetups and conferences, Direct applications on company career pages
Career Connection
Provides invaluable insights into potential career paths, facilitates crucial networking opportunities, and often leads to pre-placement offers, giving students a significant head start in their careers.
Advanced Stage
Specialize and Deepen Technical Expertise- (Semester 6-8)
Strategically choose professional electives to specialize in specific areas of data science, such as Natural Language Processing, Deep Learning, MLOps, or Reinforcement Learning. Undertake advanced projects that delve deeply into these chosen domains.
Tools & Resources
Review of advanced research papers, Specialized Massive Open Online Courses (MOOCs), Contributions to open-source data science projects
Career Connection
Developing niche and in-depth skills in a particular specialization makes graduates highly sought after by specific industry verticals and positions them for advanced, specialized roles within the data science field.
Focus on Comprehensive Major and Capstone Projects- (Semester 7-8)
Dedicate substantial effort and innovation to the Major Project (Project I & II) and the Capstone Project. Aim to develop solutions that are not only technically sound but also demonstrate a clear, measurable impact and innovation.
Tools & Resources
Guidance from faculty mentors, Collaboration with industry advisors for real-world context, Access to advanced and larger datasets, Leveraging cloud computing platforms for intensive tasks
Career Connection
A well-executed and robust project showcases exceptional problem-solving abilities, technical prowess, and strategic thinking. It serves as a significant talking point in job interviews and an invaluable addition to a professional portfolio.
Intensive Placement Preparation and Career Planning- (Semester 7-8)
Actively participate in mock interview sessions, resume building workshops, and aptitude test preparation specifically tailored for data science roles. Develop a clear career plan outlining short-term and long-term professional objectives.
Tools & Resources
University career services department, Interview preparation platforms like Glassdoor and LeetCode, Leveraging alumni network for insights and referrals
Career Connection
This comprehensive preparation maximizes the chances of securing desirable placements by honing critical interview skills, reinforcing technical knowledge, and enhancing overall professional presentation abilities, leading to a smooth transition into industry.
Program Structure and Curriculum
Eligibility:
- 10+2 with Physics and Mathematics as compulsory subjects along with one of the Chemistry/Biotechnology/Biology/Technical Vocational subject. Obtained at least 50% marks (45% in case of candidate belonging to reserved category) in the above subjects taken together. Valid score in JEE (Mains)/HPCET/Shoolini University Common Entrance Test (SUCET).
Duration: 8 semesters / 4 years
Credits: 167 Credits
Assessment: Internal: 40% (for theory subjects) / 60% (for practical subjects), External: 60% (for theory subjects) / 40% (for practical subjects)
Semester-wise Curriculum Table
Semester 1
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA101 | Engineering Mathematics-I | Core | 4 | Matrices, Differential Calculus, Integral Calculus, Ordinary Differential Equations, Partial Differential Equations |
| PH101 | Engineering Physics | Core | 4 | Oscillations & Waves, Physical Optics, Lasers & Optical Fibres, Quantum Mechanics, Solid State Physics |
| PH102 | Engineering Physics Lab | Lab | 1 | Experiments on waves, Experiments on optics, Experiments on lasers, Experiments on quantum physics, Error analysis and measurement |
| CS101 | Introduction to Computer Science & Engineering | Core | 3 | Computer Fundamentals, Programming Concepts, Data Representation, Networking Basics, Operating Systems Introduction |
| CS102 | Programming for Problem Solving | Core | 3 | Algorithms and Flowcharts, C Language Basics, Control Structures, Functions and Arrays, Pointers and Structures |
| CS103 | Programming for Problem Solving Lab | Lab | 1 | C Programming Exercises, Debugging Techniques, Problem Solving with C, File Handling Practice, Data Input/Output Operations |
| EE101 | Basic Electrical Engineering | Core | 3 | DC Circuits Analysis, AC Circuits Analysis, Transformers, Electrical Machines, Measuring Instruments |
| EE102 | Basic Electrical Engineering Lab | Lab | 1 | Verification of Circuit Laws, Measurement of Electrical Quantities, Characteristics of Electrical Components, Power Measurement, Basic Wiring Practices |
| ME101 | Engineering Graphics & Design | Core | 2 | Engineering Drawing Standards, Orthographic Projections, Isometric Projections, Sectional Views, Introduction to AutoCAD |
| ME102 | Engineering Graphics & Design Lab | Lab | 1 | Manual Drawing Exercises, CAD Software Practice, Drawing of Machine Components, Dimensioning and Tolerances, Assembly Drawing |
| HS101 | English Language | Core | 2 | Grammar and Usage, Reading Comprehension, Writing Skills, Presentation Skills, Vocabulary Building |
| HS102 | English Language Lab | Lab | 1 | Spoken English Practice, Group Discussions, Public Speaking, Interview Skills, Audio-Visual Aids |
Semester 2
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA201 | Engineering Mathematics-II | Core | 4 | Multivariable Calculus, Vector Calculus, Laplace Transforms, Fourier Series, Complex Analysis |
| CH101 | Engineering Chemistry | Core | 4 | Water Technology, Fuels & Lubricants, Polymers & Composites, Corrosion and its Control, Spectroscopic Techniques |
| CH102 | Engineering Chemistry Lab | Lab | 1 | Water Analysis Experiments, Fuel Property Determination, Polymer Synthesis, Corrosion Measurement, Titration and Volumetric Analysis |
| CS201 | Data Structures | Core | 3 | Arrays and Linked Lists, Stacks and Queues, Trees and Binary Trees, Graphs and Graph Traversal, Sorting and Searching Algorithms |
| CS202 | Data Structures Lab | Lab | 1 | Implementation of Linked Lists, Stack and Queue Operations, Tree Traversal Algorithms, Graph Representation and Algorithms, Sorting and Searching Implementations |
| EC101 | Basic Electronics Engineering | Core | 3 | Semiconductor Diodes, Transistors (BJT, MOSFET), Rectifiers and Filters, Amplifiers and Oscillators, Digital Logic Gates |
| EC102 | Basic Electronics Engineering Lab | Lab | 1 | Diode Characteristics, Transistor Amplifier Circuits, Rectifier Circuits, Logic Gate Verification, Breadboard Prototyping |
| CS203 | Object Oriented Programming | Core | 3 | OOP Concepts (Encapsulation, Abstraction), Classes and Objects, Inheritance and Polymorphism, Exception Handling, Templates and STL |
| CS204 | Object Oriented Programming Lab | Lab | 1 | C++ or Java Programming, Class and Object Implementation, Inheritance and Polymorphism Exercises, Exception Handling Practice, File Operations in OOP |
| ME201 | Manufacturing Practices | Core | 2 | Workshop Safety, Carpentry and Joinery, Welding Processes, Machining Operations, Fitting and Assembly |
| ME202 | Manufacturing Practices Lab | Lab | 1 | Hands-on Carpentry, Welding Practice, Lathe Machine Operations, Benchwork and Fitting, Sheet Metal Working |
| EV101 | Environmental Studies | Core | 2 | Ecosystems and Biodiversity, Environmental Pollution, Natural Resources Management, Global Environmental Issues, Sustainable Development |
Semester 3
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| MA301 | Discrete Mathematics | Core | 4 | Mathematical Logic, Set Theory and Relations, Functions and Sequences, Graph Theory, Algebraic Structures |
| CS301 | Computer Organization & Architecture | Core | 3 | Digital Logic Circuits, Data Representation, CPU Organization, Memory Hierarchy, Input/Output Organization |
| CS302 | Operating Systems | Core | 3 | Operating System Concepts, Process Management, Memory Management, File Systems, I/O Management |
| CS303 | Operating Systems Lab | Lab | 1 | Linux Commands and Utilities, Shell Scripting, Process Synchronization Problems, Memory Allocation Simulation, File System Calls |
| DS301 | Introduction to Data Science | Core | 3 | Data Science Workflow, Data Types and Sources, Data Collection and Cleaning, Exploratory Data Analysis, Basic Data Visualization |
| DS302 | Introduction to Data Science Lab | Lab | 1 | Python/R for Data Analysis, Data Manipulation with Pandas, Basic Statistical Computing, Data Cleaning Techniques, Introductory Visualization with Matplotlib |
| DS303 | Probability and Statistics for Data Science | Core | 3 | Probability Theory, Random Variables and Distributions, Descriptive Statistics, Inferential Statistics, Hypothesis Testing and Regression |
| DS304 | Probability and Statistics for Data Science Lab | Lab | 1 | Statistical Software (R/Python), Data Distribution Analysis, Hypothesis Testing Implementations, Regression Analysis, Confidence Intervals Calculation |
| HS301 | Universal Human Values | Core | 3 | Self-exploration and Self-awareness, Human Relationships and Harmony, Understanding Society, Harmony with Nature, Professional Ethics |
Semester 4
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS401 | Database Management Systems | Core | 3 | Relational Model, SQL Query Language, ER Diagrams and Schema Design, Normalization, Transaction Management |
| CS402 | Database Management Systems Lab | Lab | 1 | SQL Queries and Joins, Database Creation and Manipulation, Stored Procedures and Functions, Trigger Implementation, Database Connectivity (e.g., Python-SQL) |
| CS403 | Design & Analysis of Algorithms | Core | 3 | Algorithm Efficiency, Asymptotic Notations, Divide and Conquer, Dynamic Programming, Graph Algorithms |
| CS404 | Design & Analysis of Algorithms Lab | Lab | 1 | Implementation of Sorting Algorithms, Graph Traversal Algorithms, Dynamic Programming Solutions, Greedy Algorithm Implementations, Time and Space Complexity Analysis |
| DS401 | Machine Learning | Core | 3 | Supervised Learning (Regression, Classification), Unsupervised Learning (Clustering), Model Evaluation Metrics, Bias-Variance Tradeoff, Ensemble Methods |
| DS402 | Machine Learning Lab | Lab | 1 | Scikit-learn for ML, Linear and Logistic Regression, Decision Trees and SVMs, Clustering Algorithms (K-Means), Model Hyperparameter Tuning |
| DS403 | Data Visualization | Core | 3 | Principles of Data Visualization, Matplotlib and Seaborn Libraries, Interactive Visualizations, Storytelling with Data, Dashboard Design |
| DS404 | Data Visualization Lab | Lab | 1 | Plotting with Matplotlib, Advanced Visualizations with Seaborn, Interactive Plotting (Plotly/Bokeh), Dashboard Creation (Power BI/Tableau basics), Customizing Visualizations |
| OE001 | Open Elective – I | Elective | 3 |
Semester 5
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS501 | Software Engineering | Core | 3 | Software Development Life Cycle, Requirements Engineering, Software Design Principles, Software Testing and Maintenance, Software Project Management |
| DS501 | Big Data Analytics | Core | 3 | Introduction to Big Data, Hadoop Ecosystem (HDFS, MapReduce), Apache Spark, NoSQL Databases, Big Data Processing Frameworks |
| DS502 | Big Data Analytics Lab | Lab | 1 | Hadoop Installation and Configuration, MapReduce Programming, Spark RDD and DataFrame Operations, HBase/MongoDB Interactions, Data Ingestion with Sqoop/Flume |
| DS503 | Deep Learning | Core | 3 | Neural Network Fundamentals, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Transfer Learning, Deep Learning Frameworks (TensorFlow/PyTorch) |
| DS504 | Deep Learning Lab | Lab | 1 | Implementing Feedforward Networks, Building CNNs for Image Classification, Developing RNNs for Sequence Data, Fine-tuning Pre-trained Models, Using GPUs for Training |
| DS505 | Data Warehousing & Data Mining | Core | 3 | Data Warehouse Architecture, OLAP Operations, Data Mining Techniques, Association Rule Mining, Clustering Algorithms |
| DS506 | Data Warehousing & Data Mining Lab | Lab | 1 | Designing ETL Processes, OLAP Cube Operations, Implementing Association Rules, Clustering with K-Means/DBSCAN, Data Mining Tools (Weka/RapidMiner) |
| OE002 | Open Elective – II | Elective | 3 | |
| DS511 | Professional Elective – I (Data Ethics and Privacy) | Elective | 3 | Ethical AI Principles, Data Privacy Regulations (GDPR, Indian Laws), Bias in AI Systems, Data Governance Frameworks, Responsible AI Development |
| DS507 | Minor Project | Project | 2 | Problem Identification, Requirement Analysis, Design and Implementation, Testing and Evaluation, Project Documentation and Presentation |
Semester 6
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| CS601 | Computer Networks | Core | 3 | OSI and TCP-IP Models, Network Devices and Topologies, IP Addressing and Routing, Application Layer Protocols (HTTP, FTP), Network Security Basics |
| CS602 | Computer Networks Lab | Lab | 1 | Network Simulation Tools (Packet Tracer), Socket Programming (TCP/UDP), Network Configuration Commands, Packet Sniffing and Analysis, Basic Network Security Implementations |
| DS601 | Natural Language Processing | Core | 3 | Text Preprocessing, NLP Tasks (Tokenization, POS Tagging), Word Embeddings (Word2Vec, GloVe), Sequence Models (RNNs, LSTMs), Transformer Networks (BERT, GPT) |
| DS602 | Natural Language Processing Lab | Lab | 1 | NLTK and SpaCy Libraries, Text Classification, Sentiment Analysis, Machine Translation Models, Named Entity Recognition |
| DS603 | Cloud Computing for Data Science | Core | 3 | Cloud Service Models (IaaS, PaaS, SaaS), Cloud Deployment Models, Major Cloud Providers (AWS, Azure, GCP), Data Science Workflows on Cloud, Serverless Computing for ML |
| DS604 | Cloud Computing for Data Science Lab | Lab | 1 | Setting up Cloud Environments, Deploying ML Models on Cloud, Using Cloud Storage and Databases, Serverless Function Deployment, Cost Optimization in Cloud |
| DS611 | Professional Elective – II (Time Series Analysis) | Elective | 3 | Components of Time Series, ARIMA and SARIMA Models, Exponential Smoothing, Forecasting Techniques, Seasonality and Trend Analysis |
| OE003 | Open Elective – III | Elective | 3 | |
| HS601 | Constitution of India | Core | 0 | Preamble and Fundamental Rights, Directive Principles of State Policy, Union and State Legislature, Indian Judiciary, Constitutional Amendments |
| DS605 | Internship/Industrial Training | Internship | 3 | Industry Specific Projects, Professional Skill Development, Real-world Problem Solving, Team Collaboration, Report Writing and Presentation |
Semester 7
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DS711 | Professional Elective – III (Reinforcement Learning) | Elective | 3 | Markov Decision Processes, Q-Learning and SARSA, Deep Reinforcement Learning, Policy Gradient Methods, Exploration vs. Exploitation |
| DS712 | Professional Elective – IV (MLOps) | Elective | 3 | ML Lifecycle Management, Model Deployment Strategies, Model Monitoring and Maintenance, Version Control for ML Assets, CI/CD for Machine Learning |
| DS713 | Professional Elective – V (Data Security and Privacy) | Elective | 3 | Cryptography in Data Science, Access Control Mechanisms, Data Anonymization Techniques, Privacy-Preserving Machine Learning, Data Breach Incident Response |
| OE004 | Open Elective – IV | Elective | 3 | |
| DS701 | Project – I | Project | 4 | Advanced Problem Definition, Literature Survey and Research, System Design and Architecture, Initial Implementation and Prototyping, Progress Reporting |
| DS702 | Capstone Project | Project | 2 | Integrative Project Application, Interdisciplinary Problem Solving, Culminating Design and Development, Comprehensive Presentation, Final Documentation |
Semester 8
| Subject Code | Subject Name | Subject Type | Credits | Key Topics |
|---|---|---|---|---|
| DS801 | Project – II | Project | 12 | Final System Implementation, Testing and Validation, Performance Optimization, Comprehensive Project Report, Oral Presentation and Defense |
